infrared radiance
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2022 ◽  
Vol 270 ◽  
pp. 112856
Author(s):  
Yelu Zeng ◽  
Min Chen ◽  
Dalei Hao ◽  
Alexander Damm ◽  
Grayson Badgley ◽  
...  

2022 ◽  
Vol 14 (2) ◽  
pp. 407
Author(s):  
Jongjin Seo ◽  
Haklim Choi ◽  
Young-Suk Oh

Aerosols in the atmosphere play an essential role in the radiative transfer process due to their scattering, absorption, and emission. Moreover, they interrupt the retrieval of atmospheric properties from ground-based and satellite remote sensing. Thus, accurate aerosol information needs to be obtained. Herein, we developed an optimal-estimation-based aerosol optical depth (AOD) retrieval algorithm using the hyperspectral infrared downwelling emitted radiance of the Atmospheric Emitted Radiance Interferometer (AERI). The proposed algorithm is based on the phenomena that the thermal infrared radiance measured by a ground-based remote sensor is sensitive to the thermodynamic profile and degree of the turbid aerosol in the atmosphere. To assess the performance of algorithm, AERI observations, measured throughout the day on 21 October 2010 at Anmyeon, South Korea, were used. The derived thermodynamic profiles and AODs were compared with those of the European center for a reanalysis of medium-range weather forecasts version 5 and global atmosphere watch precision-filter radiometer (GAW-PFR), respectively. The radiances simulated with aerosol information were more suitable for the AERI-observed radiance than those without aerosol (i.e., clear sky). The temporal variation trend of the retrieved AOD matched that of GAW-PFR well, although small discrepancies were present at high aerosol concentrations. This provides a potential possibility for the retrieval of nighttime AOD.


2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stan Schymanski ◽  
Ivonne Trebs ◽  
Mauro Suils ◽  
Kaniska Mallic

Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. At the plot-scale, the flux tower recorded longwave radiation components are inverted to retrieve LST. Since the down-welling longwave component was not measured routinely until recently, usually only the up-welling longwave component is used for the plot-scale LST retrieval. However, we found that ignoring reflected down-welling longwave radiation for plot-scale LST estimations can lead to substantial error. This also has important implications for estimating the correct surface emissivity using flux tower measurements, which is needed for plot-scale LST retrievals. The present study proposes a new method for plot-scale emissivity and LST estimation and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both up-welling and down-welling longwave radiation components are 0.5 to 1.5 K lower than estimates using only up-welling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the up-welling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. Landscape-scale day-time LST obtained from satellite data (MODIS TERRA) was strongly correlated with our plot-scale estimates for most of the sites, but higher by several Kelvin at two sites. We also quantified the uncertainty in estimated LST and surface emissivity using the different methods and found that the proposed method does not result in increased uncertainty. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.


2021 ◽  
Author(s):  
Gitanjali Thakur ◽  
Stanislaus J. Schymanski ◽  
Ivonne Trebs ◽  
Mauro Sulis ◽  
Kaniska Mallick

Abstract Land surface temperature (LST) is a preeminent state variable that controls the energy and water exchange between the Earth’s surface and the atmosphere. At the landscape-scale, LST is derived from thermal infrared radiance measured using space-borne radiometers. At the plot-scale, the flux tower recorded longwave radiation components are inverted to retrieve LST. Since the down-welling longwave component was not measured routinely until recently, usually only the up-welling longwave component is used for the plot-scale LST retrieval. However, we found that ignoring reflected down-welling longwave radiation for plot-scale LST estimations can lead to substantial error. This also has important implications for estimating the correct surface emissivity using flux tower measurements, which is needed for plot-scale LST retrievals. The present study proposes a new method for plot-scale emissivity and LST estimation and addresses in detail the consequences of omitting down-welling longwave radiation as frequently done in the literature. Our analysis uses ten eddy covariance sites with different land cover types and found that the LST values obtained using both up-welling and down-welling longwave radiation components are 0.5 to 1.5 K lower than estimates using only up-welling longwave radiation. Furthermore, the proposed method helps identify inconsistencies between plot-scale radiometric and aerodynamic measurements, likely due to footprint mismatch between measurement approaches. We also found that such inconsistencies can be removed by slight corrections to the up-welling longwave component and subsequent energy balance closure, resulting in realistic estimates of surface emissivity and consistent relationships between energy fluxes and surface-air temperature differences. Landscape-scale daytime LST obtained from satellite data (MODIS TERRA) was strongly correlated with our plot-scale estimates for most of the sites, but higher by several Kelvin at two sites. We also quantified the uncertainty in estimated LST and surface emissivity using the different methods and found that the proposed method does not result in increased uncertainty. The results of this work have significant implications for the combined use of aerodynamic and radiometric measurements to understand the interactions and feedbacks between LST and surface-atmosphere exchange processes.


2021 ◽  
Vol 15 (04) ◽  
Author(s):  
Fangfang Yu ◽  
Xiangqian Wu ◽  
Hyelim Yoo ◽  
Haifeng Qian ◽  
Xi Shao ◽  
...  

Author(s):  
Jun Li ◽  
Alan J. Geer ◽  
Kozo Okamoto ◽  
Jason A. Otkin ◽  
Zhiquan Liu ◽  
...  

AbstractSatellite infrared (IR) sounder and imager measurements have become one of the main sources of data used by data assimilation systems to generate initial conditions for numerical weather prediction (NWP) models and atmospheric analysis/reanalysis. This paper reviews the development of satellite IR data assimilation in NWP in recent years, especially the assimilation of all-sky satellite IR observations. The major challenges and future directions are outlined and discussed.


2021 ◽  
Vol 14 (8) ◽  
pp. 5717-5734
Author(s):  
Jing Feng ◽  
Yi Huang ◽  
Zhipeng Qu

Abstract. Measuring atmospheric conditions above convective storms using spaceborne instruments is challenging. The operational retrieval framework of current hyperspectral infrared sounders adopts a cloud-clearing scheme that is unreliable in overcast conditions. To overcome this issue, previous studies have developed an optimal estimation method that retrieves the temperature and humidity above high thick clouds by assuming a slab of cloud. In this study, we find that variations in the effective radius and density of cloud ice near the tops of convective clouds lead to non-negligible spectral uncertainties in simulated infrared radiance spectra. These uncertainties cannot be fully eliminated by the slab-cloud assumption. To address this problem, a synergistic retrieval method is developed here. This method retrieves temperature, water vapor, and cloud properties simultaneously by incorporating observations from active sensors in synergy with infrared radiance spectra. A simulation experiment is conducted to evaluate the performance of different retrieval strategies using synthetic radiance data from the Atmospheric Infrared Sounder (AIRS) and cloud data from CloudSat/CALIPSO. In this experiment, we simulate infrared radiance spectra from convective storms through a combination of a numerical weather prediction model and a radiative transfer model. The simulation experiment shows that the synergistic method is advantageous, as it shows high retrieval sensitivity to the temperature and ice water content near the cloud top. The synergistic method more than halves the root-mean-square errors in temperature and column integrated water vapor compared to prior knowledge based on the climatology. It can also improve the quantification of the ice water content and effective radius compared to prior knowledge based on retrievals from active sensors. Our results suggest that existing infrared hyperspectral sounders can detect the spatial distributions of temperature and humidity anomalies above convective storms.


2021 ◽  
Author(s):  
Prashant Kumar ◽  
Munn Vinayak Shukla ◽  
Atul K Varma
Keyword(s):  

2021 ◽  
Author(s):  
Niama Boukachaba ◽  
Oreste Reale ◽  
Erica L. McGrath-Spangler ◽  
Manisha Ganeshan ◽  
Will McCarty ◽  
...  

<p>Previous work by this team has demonstrated that assimilation of IR radiances in partially cloudy regions is beneficial to numerical weather predictions (NWPs), improving the representation of tropical cyclones (TCs) in global analyses and forecasts. The specific technique used by this team is based on the “cloud-clearing CC” methodology. Cloud-cleared hyperspectral IR radiances (CCRs), if thinned more aggressively than clear-sky radiances, have shown a strong impact on the analyzed representation and structure of TCs. However, the use of CCRs in an operational context is limited by 1) latency; and 2) external dependencies present in the original cloud-clearing algorithm. In this study, the Atmospheric InfraRed Sounder (AIRS) CC algorithm was (a) ported to NASA high end computing resources (HEC), (b) deprived of external dependencies, and (c) parallelized improving the processing by a factor of 70. The revised AIRS CC algorithm is now customizable, allowing user’s choice of channel selection, user’s model's fields as first guess, and could perform in real time. This study examines the benefits achieved when assimilating CCRs using the NASA’s Goddard Earth Observing System (GEOS) hybrid 4DEnVar system. The focus is on the 2017 Atlantic hurricane season with three infamous hurricanes (Harvey, Irma, and Maria) investigated in depth.  The impact of assimilating customized CCRs on the analyzed representation of tropical cyclone horizontal and vertical structure and on forecast skill is discussed.</p>


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